where we use \(\beta =0.1\) . This sequential averaging smoothes out the variance of axis lengths across iterations and helps to stabilize the optimization. This is similar in spirit to the momentum mechanism in the Stochastic Gradient Descent (SGD) methods [1]}.
Finally, we compute the scaling factor using \(\mathbf {r}_k\)  and \(\mathbf {r}_{k-1}\)  and transform the particles in PST using the function RescalePST in Algorithm REF . See the supplemental material for details.
